Performance of first- and second-order methods for \(\ell _1\) -regularized least squares problems
نویسندگان
چکیده
We study the performance of firstand second-order optimization methods for `1-regularized sparse least-squares problems as the conditioning of the problem changes and the dimensions of the problem increase up to one trillion. A rigorously defined generator is presented which allows control of the dimensions, the conditioning and the sparsity of the problem. The generator has very low memory requirements and scales well with the dimensions of the problem.
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ورودعنوان ژورنال:
- Comp. Opt. and Appl.
دوره 65 شماره
صفحات -
تاریخ انتشار 2016